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Online Orthogonal Matching Pursuit
[article]
2021
arXiv
pre-print
Greedy algorithms for feature selection are widely used for recovering sparse high-dimensional vectors in linear models. In classical procedures, the main emphasis was put on the sample complexity, with little or no consideration of the computation resources required. We present a novel online algorithm: Online Orthogonal Matching Pursuit (OOMP) for online support recovery in the random design setting of sparse linear regression. Our procedure selects features sequentially, alternating between
arXiv:2011.11117v2
fatcat:3dj3nk4svbbohkjsi6fh4rt4kq